The purpose of classification in medical informatics is to predict the presence or absence of a particular disease as well as disease types from historical data. Medical data often contain irrelevant features and noise, and an appropriate subset of the significant features can improve classification accuracy. Therefore, researchers apply feature selection to identify and remove irrelevant and redundant features. The authors propose a versatile feature selection approach called Swarm Search Feature Selection (SS-FS), based on stochastic swarm intelligence. It is designed to overcome NP-hard combinatorial search problems such as the selection of an optimal feature subset from an extremely large array of features - which is not uncommon in biomedical data. SS-FS is demonstrated to be a feasible computing tool in achieving high accuracy in classification via testing with two empirical biomedical datasets. This article is part of a special issue on life sciences computing.